Non-Intrusive Interpretation and Improvement of Multi-Occupancy Human Thermal Comfort through Analysis of Facial Infrared Thermography

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

In the U.S. and worldwide, HVAC systems represent one of the largest energy end uses, accounting for approximately 50 percent of the total energy required to operate residential and commercial buildings. Despite the significant energy footprint of HVAC systems, occupants in the built environment are often dissatisfied with their thermal comfort. Current "human-in-the-loop" approaches provide opportunities for occupants to vote on thermal comfort and preferences (for example, by adjusting the thermostat), thus allowing for HVAC system adjustment based on human feedback. However, relying on intermittent human feedback prevents robust evaluation of the comfort level and determination of a comfortable setpoint. This project will explore the feasibility of using infrared thermography as a non-intrusive method for predicting human thermal comfort preferences in single and multi-occupancy building spaces. It will also design and validate a robust HVAC control framework for buildings that will synchronously use the analyzed thermography data to adjust its setpoint to improve thermal comfort in indoor spaces and reduce overall dissatisfaction among occupants in multi-occupancy spaces. The project aims to explore the premise that thermal comfort can be measured non-intrusively and reliably in real, operational built environments. The resulting new knowledge has the potential to transition building HVAC control from a passive and user-empirical process to an automated, user-centric and data-driven mechanism that can simultaneously improve occupant satisfaction in indoor environments while reducing energy consumption. The research extends theory from human thermal comfort evaluation, computer vision and optimization under uncertainties with the objective of creating a robust and scalable non-intrusive HVAC control framework for thermal comfort optimization in various indoor contexts. Although the focus is on thermal comfort in this project, the developed framework and methodology can be extended to evaluate other indoor environmental quality factors such as lighting and airflow. This project will also build a publicly available thermal comfort dataset consisting of facial thermal images, subjective thermal sensations and preferences of occupants, and ambient room conditions, that will enable consistent evaluation and benchmarking of new methods in the future. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.